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Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner. These methods usually have the problem of insufficient extraction of spatio-temporal features of the IoV, which affects the performance of intrusion detection and leads to a high false-positive rate. To solve the above problems, this paper proposes an intrusion detection method for IoV based on parallel analysis of spatio-temporal features (PA-STF). First, we built an optimal subset of features based on feature correlations of IoV traffic. Then, we used the temporal convolutional network (TCN) and long short-term memory (LSTM) to extract spatio-temporal features in the IoV traffic in a parallel manner. Finally, we fused the spatio-temporal features extracted in parallel based on the self-attention mechanism and used a multilayer perceptron to detect attacks in the Internet of Vehicles. The experimental results show that the PA-STF method reduces the false-positive rate by 1.95% and 1.57% on the NSL-KDD and UNSW-NB15 datasets, respectively, with the accuracy and F1 score also being superior.<\/jats:p>","DOI":"10.3390\/s23094399","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:12:11Z","timestamp":1682943131000},"page":"4399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-3817","authenticated-orcid":false,"given":"Ling","family":"Xing","sequence":"first","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0209-4488","authenticated-orcid":false,"given":"Honghai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huahong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7385","DOI":"10.1109\/JSEN.2022.3153338","article-title":"Federated learning-based collaborative authentication protocol for shared data in social IoV","volume":"22","author":"Zhao","year":"2022","journal-title":"IEEE Sen. 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